Master’s Thesis: Visualization Toolbox

A visualization is created following
specific rules to encode data in visual features. Visualization Toolbox is an application
to discover, explore, and visually analyze datasets from different sources, e.g.
publications from digital libraries such as ACM and IEEE, cultural content from
services such as Europeana, or the local file system. The Toolbox relies on semantic
data models (vocabularies) and their integration (mapping) to automatically
generate and configure suitable visualizations. In a nutshell, the role of the
Toolbox is to support automated process of providing visualizations for
exploring and analyzing data sets from different sources (see Figure 1).

Figure 1:
Toolbox workflow for generating visualizations

Student TasksYou will
be working on three main tasks:

Task 1 – Data Selection and Transformation. Data attributes are properties that
intrinsically describe a piece of data. The first task of this work is the implementation
of an interface for retrieving data from different sources and transforming and
extracting it’s defining properties. This task involves a proper design so that
new data sources can easily be integrated in the future.

Task 2 – Mapping Algorithm. A mapping algorithm encodes data into visual features
of a visualization. One way to do so is to map data attributes (extracted in
Task 1) to visual variables (visual features of a visualization). This task is
about implementing of a well-known algorithm to automatically map data onto
visualizations. The algorithm takes the transformed data as input and suggests appropriate
visualizations. The implementation should entirely work on the client side,
i.e. within the local browser instance. An existing, external visualization
recommender is used to rank the suggested visualizations depending on user’s profile.

Part
of the task is to devise a technical solution for using OWL/RDF in the browser,
which is required to describe the data (RDF Data Cube vocabulary) and the
visualization components (Visual Analytics vocabulary). The two ontologies are
already available.

Task 3 – Visual Interface. This task involves the implementation of the actual user
interface, which provides multiple visualizations for interactive data
analysis. The task here is not to develop novel visualizations, but to reuse
existing chart implementations (from a library like d3.js) to provide the
following functionality:

·User
feedback:
Rating and tagging the suggested visualization(s). The feedback contributes to
user’s profile, which is passed to the visualization recommender to provide a
better ranking of the visualizations. The visualization recommender is already available.

·Extensibility: Possibility to upload
and add new, custom visualization implementations to the toolbox. A visualization
implementation consists of JavaScript code and semantic descriptions of the
visualization.

·Interactive
analysis:
Data exploration is supported through interactions such as brushing and
filtering, data aggregation, or showing additional details on demand.